{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring photon heights with ICESat-2 (ATL03)\n",
    "\n",
    "Information obtained primarily from the ATL03 Algorithm Theoretical Basis Document (ATBD, Neumann et al., 2019) and the NSIDC product description page: https://nsidc.org/data/atl03.   \n",
    "\n",
    "* Notebook author: Alek Petty (relying extensively on the ATBD and product description)    \n",
    "* Description: Notebook describing the ICESat-2 ATL03 product.   \n",
    "* Input requirements: Any example ATL03 data file.   \n",
    "* Date: June 2019\n",
    "* More info: See the ATL03 Algorithm Theoretical Basis Document (ATBD): https://icesat-2.gsfc.nasa.gov/sites/default/files/page_files/ICESat2_ATL03_ATBD_r001.pdf and the known issues document: https://nsidc.org/sites/nsidc.org/files/technical-references/ATL03_Known_Issues_May2019.pdf\n",
    "\n",
    "## Notebook objectives\n",
    "* General understanding of the data included in a typical ATL03 file.\n",
    "* Reading in, plotting and basic analysis of ATL03 data.\n",
    "* What we can learn from ATL03 to derive the ATL07 surface height segments!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook instructions\n",
    "1. Follow along with the notebook tutorial. \n",
    "2. Play around changing options and re-running the relevant notebook cells. \n",
    "\n",
    "Here I use the HDF5 ATL03 file (ATL03_20181115022655_07250104_001_01.h5) obtained from: https://nsidc.org/data/atl03. See the NSIDC tutorial for more information on generating granules of data to use in this tutorial. If using this using the ICESat-2 Pangeo instance, you can download the file from Amazon S3 using the notebook cell provided below.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ATL03 Background\n",
    "\n",
    "*NB: This is my laymans description of ATL03 compiled from the above resources, trying to condense this information to the key points of interest to potential sea ice users. let me know if you see any errors!*\n",
    "\n",
    "ATL03 is a Level 2A ICESat-2 data product available from the NSIDC: https://nsidc.org/data/atl03. It's assumed that ATL03 will be the lowest level product of interest to sea ice users of ICESat-2 data. ATL03 is based on the ICESat-2 Level 1B data product (ATL02) which provides the precise round-trip time of flight of individual photons.  ATL03 combines this information with the  the observatory position and the laser pointing vectors to estimate the location of the photon surface returns relative to the WGS-84 ellipsoid. The ATL03 product is then used by the higher level (Level 3) products that provide more focussed geophysical datasets to respective user communities (e.g. ATL07 below for sea ice).\n",
    "\n",
    "The data within a given ATL03 fie includes individual photons heights, background rates, and corrections applied to the height estimates. ATL03 applies multiple geophysical corrections to the height estimates to generate a best estimate of the photon height. \n",
    "\n",
    "Additional corrections that some users may decide to apply are provided within the product. A number of meteorological parameters (e.g., wind, surface air temperature, sea level pressure, etc. from reanalyses) are also provided with ATL03. ATL03 files are big (serveral hundreds of Gbs for a given granule, hence the desire to produce and release higher level processed products to the community).\n",
    "\n",
    "ATL03 provides a coarse surface classification to enable higher-level products to easily grab the photons of interest from a given granule (land, ocean, sea ice, land ice, and inland water areas, multiple categories permitted).  \n",
    "\n",
    "The product also attempts to discriminate between signal (surface returns) and background photon events. The background photons are mainly a result of solar photons at the same frequency of the ATLAS detector (532 nm) that have scattered from clouds or the surface. The background is thus expected to drop to near-zero during nighttime. A background photon rate is calculated by expanding the vertical range of photon detection to over 10 km for more than 400 transmitted laser pulses from the strong beams. Form this expected background rate, a photon rate threhold is set to search for signal photons within vertical elevation bins. The photons are classified for the given surface type classification based on the signal-to-noise ratio: high-confidence signal (SNR > 100), medium-confidence signal (100 > SNR > 40), low confidence signal (40 > SNR > 3), or likely background (SNR < 3).  \n",
    "\n",
    "Finally, several geophysical corrections are applied to these WGS84 relative heights, including:\n",
    "* the height of the ocean pole tide\n",
    "* the height of the ocean load tide\n",
    "* the height of the solid earth pole tide\n",
    "* the solid earth tide\n",
    "* the height of the total column atmospheric delay correction.  \n",
    "\n",
    "The data product also includes (but doesn't use) the geoid (EGM2008), the ocean tide (GOT4.8) and the AVISO dynamic atmospheric correction (MOG2D) which are often used by the higher level products like ATL07.\n",
    "\n",
    "Below is an example of how one can read in and explore a given ATL03 file. Very open to suggestions on possible improvements!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Magic function to enable interactive plotting in Jupyter notebook\n",
    "#Allows you to zoom/pan within plots after generating\n",
    "#Normally, this would be %matplotlib notebook, but since we're using Juptyerlab, we need a different widget\n",
    "#%matplotlib notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "#Import necesary modules\n",
    "#Use shorter names (np, pd, plt) instead of full (numpy, pandas, matplotlib.pylot) for convenience\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "#import seaborn as sns\n",
    "import h5py  \n",
    "import s3fs\n",
    "import readers as rd\n",
    "import utils as ut\n",
    "# Use seaborn for nicer looking inline plots\n",
    "#sns.set(context='notebook', style='darkgrid')\n",
    "#st = axes_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Beam selection   \n",
    "There are 6 beams to choose from in the ICESat-2 products (3 pairs of a strong and weak beam). The energy ratio between the weak and strong beams are  approximately 1:4 and are separated by 90 m in the across-track direction. The beam pairs are separated by ~3.3 km in the across-track direction, and the strong and weak beams are separated by ~2.5 km in the along-track direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "beamStr='gt1r'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data selection    \n",
    "If running on Pangeo instance you can greab data directly from Amazon S3 like so\n",
    "Comment out the last command if you've already got the data in the Data dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pangeo-data-upload-oregon/icesat2/ATL03_20181115022655_07250104_001_01.h5', 'pangeo-data-upload-oregon/icesat2/ATL07-01_20181115003141_07240101_001_01.h5', 'pangeo-data-upload-oregon/icesat2/ATL10-01_20181115003141_07240101_001_01.h5', 'pangeo-data-upload-oregon/icesat2/readme.txt', 'pangeo-data-upload-oregon/icesat2/Clouds_and_filtering_tutorial', 'pangeo-data-upload-oregon/icesat2/Floes-are-Swell', 'pangeo-data-upload-oregon/icesat2/Snowblower', 'pangeo-data-upload-oregon/icesat2/atl03', 'pangeo-data-upload-oregon/icesat2/atl06', 'pangeo-data-upload-oregon/icesat2/atl10', 'pangeo-data-upload-oregon/icesat2/atl12', 'pangeo-data-upload-oregon/icesat2/data-access-outputs', 'pangeo-data-upload-oregon/icesat2/data-access-shapefile', 'pangeo-data-upload-oregon/icesat2/data-access-subsetted-outputs', 'pangeo-data-upload-oregon/icesat2/data-access-subsettedoutputs', 'pangeo-data-upload-oregon/icesat2/data-correction', 'pangeo-data-upload-oregon/icesat2/elevation_change_tutorial', 'pangeo-data-upload-oregon/icesat2/geospatial-analysis', 'pangeo-data-upload-oregon/icesat2/glaciersat2', 'pangeo-data-upload-oregon/icesat2/gridding-data', 'pangeo-data-upload-oregon/icesat2/ground2float', 'pangeo-data-upload-oregon/icesat2/juneauicefield', 'pangeo-data-upload-oregon/icesat2/pine_island_glims', 'pangeo-data-upload-oregon/icesat2/segtrax', 'pangeo-data-upload-oregon/icesat2/xtrak']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'../Data/ATL03_20181115022655_07250104_001_01.h5'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucket = 'pangeo-data-upload-oregon'\n",
    "fs = s3fs.S3FileSystem()\n",
    "dataDir = 'pangeo-data-upload-oregon/icesat2/'\n",
    "s3List = fs.ls(dataDir)\n",
    "print(s3List)\n",
    "ATL03file='ATL03_20181115022655_07250104_001_01.h5'\n",
    "s3File='pangeo-data-upload-oregon/icesat2/'+ATL03file\n",
    "localFilePath='../Data/'+ATL03file\n",
    "\n",
    "#fs.get(s3File, localFilePath)\n",
    "localFilePath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'file_path' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-2f95659ef81d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#file_path = '../Data/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m#ATL03_filename = 'ATL03_20181115022655_07250104_001_01.h5'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mlocalPath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfile_path\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mATL03_filename\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'file_path' is not defined"
     ]
    }
   ],
   "source": [
    "# If data file is stored locally (already grabbed from S3) uncomment the following lines and comment out the below S3 example..\n",
    "#file_path = '../Data/'\n",
    "#ATL03_filename = 'ATL03_20181115022655_07250104_001_01.h5'\n",
    "#localPath = file_path + ATL03_filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getATL03data(fileT, numpyout=False, beam='gt1l'):\n",
    "    \"\"\" Pandas/numpy ATL03 reader\n",
    "    Written by Alek Petty, June 2018 (alek.a.petty@nasa.gov)\n",
    "\n",
    "    I've picked out the variables from ATL03 I think are of most interest to sea ice users, but by no\n",
    "    means is this an exhastive list. \n",
    "    See the xarray or dictionary readers to load in the more complete ATL03 dataset\n",
    "    or explore the hdf5 files themselves (I like using the app Panpoly for this) to see what else you\n",
    "    might want\n",
    "    \n",
    "    Args:\n",
    "        fileT (str): File path of the ATL03 dataset\n",
    "        numpy (flag): Binary flag for outputting numpy arrays (True) or pandas dataframe (False)\n",
    "        beam (str): ICESat-2 beam (the number is the pair, r=strong, l=weak)\n",
    "        \n",
    "    returns:\n",
    "        either: select numpy arrays or a pandas dataframe\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    # Open the file\n",
    "    try:\n",
    "        ATL03 = h5py.File(fileT, 'r')\n",
    "    except:\n",
    "        'Not a valid file'\n",
    "        \n",
    "    lons=ATL03[beam+'/heights/lon_ph'][:]\n",
    "    lats=ATL03[beam+'/heights/lat_ph'][:]\n",
    "    \n",
    "    #  Number of seconds since the GPS epoch on midnight Jan. 6, 1980 \n",
    "    delta_time=ATL03[beam+'/heights/delta_time'][:] \n",
    "    \n",
    "    # #Add this value to delta time parameters to compute the full gps_seconds\n",
    "    atlas_epoch=ATL03['/ancillary_data/atlas_sdp_gps_epoch'][:] \n",
    "    \n",
    "    # Conversion of delta_time to a calendar date\n",
    "    # This function seems pretty convoluted but it works for now..\n",
    "    # Sure there is a simpler functionw e can use here instead.\n",
    "    temp = ut.convert_GPS_time(atlas_epoch[0] + delta_time, OFFSET=0.0)\n",
    "    \n",
    "    # Express delta_time relative to start time of granule\n",
    "    delta_time_granule=delta_time-delta_time[0]\n",
    "\n",
    "    year = temp['year'][:].astype('int')\n",
    "    month = temp['month'][:].astype('int')\n",
    "    day = temp['day'][:].astype('int')\n",
    "    hour = temp['hour'][:].astype('int')\n",
    "    minute = temp['minute'][:].astype('int')\n",
    "    second = temp['second'][:].astype('int')\n",
    "    \n",
    "    dFtime=pd.DataFrame({'year':year, 'month':month, 'day':day, \n",
    "                        'hour':hour, 'minute':minute, 'second':second})\n",
    "    \n",
    "    \n",
    "    # Primary variables of interest\n",
    "    \n",
    "    # Photon height\n",
    "    heights=ATL03[beam+'/heights/h_ph'][:]\n",
    "    print(heights.shape)\n",
    "    \n",
    "    # Flag for signal confidence\n",
    "    # column index:  0=Land; 1=Ocean; 2=SeaIce; 3=LandIce; 4=InlandWater\n",
    "    # values:\n",
    "        #-- -1: Events not associated with a specific surface type\n",
    "        #--  0: noise\n",
    "        #--  1: buffer but algorithm classifies as background\n",
    "        #--  2: low\n",
    "        #--  3: medium\n",
    "        #--  4: high\n",
    "    signal_confidence=ATL03[beam+'/heights/signal_conf_ph'][:,2] \n",
    "    \n",
    "    # Add photon rate, background rate etc to the reader here if we want\n",
    "    \n",
    "    ATL03.close()\n",
    "    \n",
    "    \n",
    "    \n",
    "    dF = pd.DataFrame({'heights':heights, 'lons':lons, 'lats':lats,\n",
    "                       'signal_confidence':signal_confidence, \n",
    "                       'delta_time':delta_time_granule})\n",
    "    \n",
    "    # Add the datetime string\n",
    "    dFtimepd=pd.to_datetime(dFtime)\n",
    "    dF['datetime'] = pd.Series(dFtimepd, index=dF.index)\n",
    "    \n",
    "    # Filter out high elevation values \n",
    "    #dF = dF[(dF['signal_confidence']>2)]\n",
    "    # Reset row indexing\n",
    "    #dF=dF.reset_index(drop=True)\n",
    "    return dF\n",
    "    \n",
    "    # Or return as numpy arrays \n",
    "    # return along_track_distance, heights\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read in the data using the pandas reader above. Copied from the readers.py script.\n",
    "\n",
    "Take a look at the top few rows (change the number in head to increase this..)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4699046,)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>heights</th>\n",
       "      <th>lons</th>\n",
       "      <th>lats</th>\n",
       "      <th>signal_confidence</th>\n",
       "      <th>delta_time</th>\n",
       "      <th>datetime</th>\n",
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       "      <td>2018-11-15 02:32:04</td>\n",
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      ],
      "text/plain": [
       "            heights      lons       lats  signal_confidence  delta_time  \\\n",
       "4699041  938.400330  4.483219  79.994307                  4  309.159707   \n",
       "4699042  942.420593  4.483201  79.994295                  4  309.159907   \n",
       "4699043  944.992249  4.483200  79.994295                  4  309.159907   \n",
       "4699044  951.123047  4.483134  79.994245                  4  309.160707   \n",
       "4699045  933.075745  4.483064  79.994183                  4  309.161707   \n",
       "\n",
       "                   datetime  \n",
       "4699041 2018-11-15 02:32:04  \n",
       "4699042 2018-11-15 02:32:04  \n",
       "4699043 2018-11-15 02:32:04  \n",
       "4699044 2018-11-15 02:32:04  \n",
       "4699045 2018-11-15 02:32:04  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dF03= getATL03data(localFilePath, beamStr)\n",
    "dF03.tail(5)\n",
    "\n",
    "# Other readers available in readers.py script"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Map the data for visual inspection using Cartopy \n",
    "*NB (Basemap is often used for mapping but is not being officially supported by the community anymore)*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 630x630 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a shorted version for mapping purposes\n",
    "dF03short=dF03.iloc[::10000, :]\n",
    "dF03short.head(5)\n",
    "\n",
    "var='heights'\n",
    "\n",
    "plt.figure(figsize=(7,7), dpi= 90)\n",
    "# Make a new \"NorthPolarStereo\" projection instance\n",
    "ax = plt.axes(projection=ccrs.NorthPolarStereo(true_scale_latitude=70))\n",
    "plt.scatter(dF03short['lons'], dF03short['lats'],c=dF03short[var], cmap='viridis', transform=ccrs.PlateCarree())\n",
    "\n",
    "ax.coastlines()\n",
    "#ax.drawmeridians()\n",
    "plt.colorbar(label=var, shrink=0.5, extend='both')\n",
    "\n",
    "# Limit the map to -60 degrees latitude and below.\n",
    "ax.set_extent([-180, 180, 90, 60], ccrs.PlateCarree())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the photon heights of this section\n",
    "*NB: Could use seaborn for this (for one line plots) but using matplotlib for increased user flexibility.*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(dF03['datetime'],dF03['heights'], color='k', marker='.', linestyle='None', alpha=0.3)\n",
    "#plt.xlabel('Delta time (s)')\n",
    "plt.ylabel('Photon heights (m)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the photon heights but now use the signal confidence to label the markers.\n",
    "\n",
    "#np.size(np.where(dF03['signal_confidence']==3))\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(dF03[(dF03['signal_confidence']<2)]['delta_time'],dF03[(dF03['signal_confidence']<2)]['heights'], label='other', color='g', marker='.', linestyle='None', alpha=0.3)\n",
    "plt.plot(dF03[(dF03['signal_confidence']==2)]['delta_time'],dF03[(dF03['signal_confidence']==2)]['heights'], label='low', color='b', marker='.', linestyle='None', alpha=0.3)\n",
    "plt.plot(dF03[(dF03['signal_confidence']==3)]['delta_time'],dF03[(dF03['signal_confidence']==3)]['heights'], label='medium', color='c', marker='.', linestyle='None', alpha=0.1)\n",
    "plt.plot(dF03[(dF03['signal_confidence']==4)]['delta_time'],dF03[(dF03['signal_confidence']==4)]['heights'], label='high', color='m', marker='.', linestyle='None', alpha=0.1)\n",
    "plt.legend(loc=1)\n",
    "plt.xlabel('Delta time (s)')\n",
    "plt.ylabel('Photon heights (m)')\n",
    "plt.show()\n",
    "\n",
    "# Another (more elegant but less flexible) of doing a plot like this is using seaborn...\n",
    "#plt.figure(figsize=(12, 5))\n",
    "#sns.pairplot(x_vars=[\"delta_time\"], y_vars=[\"heights\"], data=dF03, \n",
    "#hue=\"signal_confidence\", size=5)\n",
    "#plt.gca().set_ylim((0, 50000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's do some basic filtering of the data! \n",
    "*Note that for sea ice our task is easier than land ice, as we know our returns should be coming from a narrow window around sea level*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's only keep the high confidence photons\n",
    "dF03ice = dF03[(dF03['signal_confidence']>3)]\n",
    "\n",
    "# ...and let's apply a hight window around the surface (+/- 50 m)\n",
    "dF03ice = dF03ice[(abs(dF03ice['heights'])<50)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data like before\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(dF03ice['delta_time'],dF03ice['heights'], label='high confidence surface returns', color='m', marker='.', linestyle='None', alpha=0.3)\n",
    "# plot a rolling mean of the photon heights. 150 photons is used in the generation of ATL07\n",
    "plt.plot(dF03ice['delta_time'].rolling(150).mean(),dF03ice['heights'].rolling(150).mean(), label='rolling mean (150 photon window)', color='k', linestyle='-', alpha=0.3)\n",
    "plt.legend(loc=2, frameon=False)\n",
    "plt.xlabel('Delta time (s)')\n",
    "plt.ylabel('Photon heights (m)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We now need to make extra corrections to the data not included in ATL03 (ocean tides, geoid, DAC?)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Extra ideas\n",
    "\n",
    "1. Check out the surface classification (check it's all sea ice or open water?!)\n",
    "2. Play around removing the corrections and see what that does to the heights.\n",
    "3. ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Onwards to the ATL07 Notebook...!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
